TUBERCULOSIS COUNTER (TC) AS THE EQUIPMENT TO MEASURE THE LEVEL OF TB IN SPUTUM

Authors

  • Fendy Purwanda
    ijtidunair@gmail.com
    Student of Biomedical Engineering S1 Study Program, Physics Department, Faculty of Science and Technology Universitas Airlangga, Indonesia
  • Yufan Fibriawan Student of Physics S1 Study Program, Physics Department, Faculty of Science and Technology Universitas Airlangga, Indonesia
  • Dyar Sasmito Bachelor of Automated System Instrumentation Vocation Study Program, Physics Department, Faculty of Science and Technology Universitas Airlangga, Indonesia
  • Fatkhunisa Rahmawati Student of Biomedical Engineering S1 Study Program, Physics Department, Faculty of Science and Technology Universitas Airlangga, Indonesia
  • Prihartini Widiyanti Biomedical Engineering S1 Study Program, Physics Department, Faculty of Science and Technology Universitas Airlangga Institute of Tropical Disease Universitas Airlangga, Indonesia

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Tuberculosis (TB) is an infectious disease caused by Mycobacterium tuberculosis. This disease is the third killer disease aftercardiovascular diseases and respiratory diseases, and is also he number one killer disease in a group of infectious diseases. This is partly due to the late handling and a non real time detection, both of which will inhibit the therapy which yields a large number
of microorganisms in the body, and will eventually complicate the recovery. Based on this phenomenon, we offered an alternativesolution for detecting the sum of microorganism using Tuberculosis Counter, a tool used to count the number of Tuberculosis bacteria in the patient's sputum. Technically, the patient's sputum preparat was screened using the TCS230 color sensor that was able to filter the color of the preparat. Tuberculosis bacteria in the stained sputum Ziehl-Niellsen preparat was colored red, while the other was colored blue. By utilizing these optical phenomena, the TCS230 color sensor was supposed to filter the red color in the preparat. By using regression equation measurement, we gained the equation which then correlated the bit value as an output of the sensor with the number of Tuberculosis bacteria. Then, the digitalization process yielded the real time and accurate data of Tuberculosis bacteria.

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